Aspects of the present disclosure involve a system and method for performing operations comprising providing to a client device, a messaging application comprising multiple features; accessing a configuration rule that associates a device property rule with a feature; determining at a first point in time, that a property of the client device matches the device property rule associated with the configuration rule; in response to determining that the property of the client device matches the device property rule associated with the configuration rule, enabling the feature on the client device at the first point in time; receiving an updated property of the client device at a second point in time; and in response to determining that the updated property of the client device fails to match the device property rule associated with the configuration rule at the second point in time, disabling the feature on the client device.

Systems for web page or web application instrumentation. Embodiments commence upon identification of a computer-readable user interface description comprising at least some markup language conforming to a respective document object model that is codified in a computer-readable language. An injector process modifies the user interface description by inserting markup text and code into the user interface description, where the inserted code includes instrumentation code to invoke dynamic generation of instrumentation locator IDs using the hierarchical elements found in the document object model. The modified computer-readable interface description is transmitted to a user device. Log messages are emitted upon user actions taken while using the user device. The log messages comprise the instrumentation locator IDs that are formed using hierarchical elements found in the document object model.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Various example embodiments for supporting device telemetry control are presented. Various example embodiments may provide a customer of a device, which is monitoring the device based on device telemetry whereby the device exposes device data of the device based on device telemetry control information of the device such that the data of the device may be accessed by the customer, with control over device telemetry of the device. Various example embodiments may provide a customer, which may access device data of a device based on device telemetry supported by the device, with additional control over access to the device data of the device via device telemetry by providing the customer with control over the device telemetry including enabling the customer to insert customer device telemetry control information into the device telemetry control information of the device that controls device telemetry on the device.

Various example embodiments for supporting device telemetry control are presented. Various example embodiments may provide a customer of a device, which is monitoring the device based on device telemetry whereby the device exposes device data of the device based on device telemetry control information of the device such that the data of the device may be accessed by the customer, with control over device telemetry of the device. Various example embodiments may provide a customer, which may access device data of a device based on device telemetry supported by the device, with additional control over access to the device data of the device via device telemetry by providing the customer with control over the device telemetry including enabling the customer to insert customer device telemetry control information into the device telemetry control information of the device that controls device telemetry on the device.

A patient record gateway of an electronic health record system can be validated using a conformance statement that defines capabilities and characteristics of patient record servers associated with the gateway. Part of validating the patient record gateway includes performing a configuration test of the patient record gateway using the conformance statement.

Described embodiments provide for routing remote application data. A device can receive a request to access an application. The application can be provided by data centers and accessible via service providers. The device can select a data center from the plurality of data centers and a service provider based at least on a metric indicative of a connection between the data center and the service provider. The device can query a database including one or more connection metrics using the application identified in the request and a location of a router transmitting the request. The device can determine the location of the router based on an internet protocol (IP) address of a client communicably coupled to the router. The device can transmit a response to the request identifying the selected data center and the selected service provider.

A variety of systems and methods can include evaluation of human user effort data. Various embodiments apply techniques to identify anomalous effort data for the purpose of detecting the efforts of a single person, as well as to segment and isolate multiple persons from a single collection of data. Additional embodiments describe the methods for using real-time anomaly detection systems that provide indicators for scoring effort data in synthesized risk analysis. Other embodiments include approaches to distinguish anomalous effort data when the abnormalities are known to be produced by a single entity, as might be applied to medical research and enhance sentiment analysis, as well as detecting the presence of a single person's effort data among multiple collections, as might be applied to fraud analysis and insider threat investigations. Embodiments include techniques for analyzing the effects of adding and removing detected anomalies from a given collection on subsequent analysis.

Provided is an anomaly pattern detection system including an anomaly detection device connected to one or more servers. The anomaly detection device may include an anomaly detector configured to model input data by considering all of the input data as normal patterns, and detect an anomaly pattern from the input data based on the modeling result.

A model correction tool automatically detects and corrects model drift in a model for a machine learning application. To detect drift, the tool continuously monitors input data, outputs, and/or technical resources (e.g., processor, memory, network, and input/output resources) used to generate outputs. The tool analyzes changes to input data, outputs, and/or resource usage to determine when drift has occurred. When drift is determined to be occurring, the tool retrains a model for a machine learning application.